Three-dimensional radiotherapy dose distribution prediction

ABSTRACT

Generating a three-dimensional radiation dose matrix for a patient for controlling the delivery of radiation dose to patients. The three-dimensional radiation dose matrix for the patient based on an intensity of radiation fields delivered by a radiation therapy delivery system that intersect with volume elements of a patient and determined by a predictive model. The intensity of the radiation fields at volume elements of the patient determined from spatial position data of the volume elements in a patient and radiation therapy delivery system data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 15/528,734 filed May 22, 2017, entitled“THREE-DIMENSIONAL RADIOTHERAPY DOSE DISTRIBUTION PREDICTION,” which isa national-phase entry of Patent Cooperation Treaty Application No.PCT/US2015/062013, which has an international filing date of Nov. 20,2015, entitled “THREE-DIMENSIONAL RADIOTHERAPY DOSE DISTRIBUTIONPREDICTION,” which claims the benefit of and priority to U.S.Provisional Patent Application No. 62/082,860, filed on Nov. 21, 2014,entitled “THREE-DIMENSIONAL RADIOTHERAPY DOSE DISTRIBUTION PREDICTION,”and U.S. Provisional Patent Application No. 62/184,141, filed on Jun.24, 2015, entitled “THREE-DIMENSIONAL RADIOTHERAPY DOSE DISTRIBUTIONPREDICTION,” the entire contents of these applications are incorporatedherein by reference in their entirety.

TECHNICAL FIELD

The subject matter disclosed herein relates to radiotherapy dosedistribution prediction.

BACKGROUND

Radiation therapy, or radiotherapy, is therapy using ionizing radiation.Radiotherapy is typically administered as part of cancer treatment tocontrol or kill malignant cells. Radiation therapy can be curative inthe treatment of some types of cancer when localized to the parts of thebody that contain cancer cells. Radiation therapy may also be used toprevent tumor recurrence after surgery to remove a primary tumor.

Radiation therapy works by the ionizing radiation damaging the DNA ofcancerous tissue leading to cellular death. Ionizing radiation caninclude charged massive particles that can ionize atoms directly throughfundamental interactions with the atoms. Such particles can includeatomic nuclei, electrons. Photons, charges ions, protons, and energeticcharge nuclei stripped of their electrons. Photons can also ionize atomsdirectly through the photoelectric effect and the Compton Effect Photonstypically cause an atom to eject an electron at relativistic speeds,turning the electron into a beta particle which goes on to ionize manyother atoms.

Ionizing radiation can damage the DNA of healthy tissue just as it doescancerous tissue. There are a number of techniques that are employed toreduce the likelihood of destroying healthy tissue that is in the pathof the ionizing radiation. One such method is to provide shapedradiation beams. The radiation beams can be shaped to mimic thecross-section of a tumor. This minimizes the amount of healthy tissuethat the radiation beam intersects. Other techniques include aimingmultiple radiation beams, or shaped radiation beams, from differentangles around the patient, such that each beam travels through adifferent path in the patient to reach the tumor. The radiation beamstypically intersect within the boundaries of the tumor, therebydelivering a much greater dose of radiation to the cancerous cells,compared to the health tissue surrounding the tumor. Margins aretypically provided to account for uncertainties in the location of thetumor caused by patient movement during treatment, equipment set-upvariations, and the like.

Imaging systems, such as computerized tomography (CT) scanners, andmagnetic resonance imaging (MRI), can be used to delineate tumors andadjacent healthy structures in three-dimensions. Virtual simulationbased on the three-dimensional images of the patient, can allow forincreased accuracy of the placement of radiation beams compared tosystems relying on more conventional imaging systems.

Three-dimensional conformal radiation therapy (3DCRT) can be used, inwhich the profile of each radiation beam is shaped to conform to theprofile of the tumor, or treatment target of the patient, from a beam'seye view. Shape confirmation can be achieved using a multileafcollimator. A variable number of beams can also be used to moreaccurately fit the shape of the tumor. Conforming the radiation therapybeams to the shape of the tumor reduces the radiation toxicity tosurrounding tissue. Consequently, the dose of radiation provided to thetumor can be increased.

Intensity-modulated radiation therapy (IMRT) can tailor the strength ofthe radiation delivered to various parts of the tumor. This isespecially effective when a tumor surrounds an important bodilystructure, such as the spinal cord. IMRT is typically performed with theuse of computer-controlled x-ray accelerators distributing preciseradiation doses across the tumor. The radiation dose intensity can becontrolled, or modulated to make the radiation dose consistent with thethree-dimensional shape of the tumor. The radiation dose intensity canbe elevated near the gross tumor volume, while decreased around theneighboring tissue.

Volumetric modulated are therapy (VMAT) can achieve highly conformaldose distributions on the treatment target, sparing normal tissue. VMATcan use a rotating gantry to deliver radiation therapy, changing theshape and speed of the radiation beam as well as the dose rate.

Other types of radiation therapy include particle therapy, augertherapy, brachytherapy, and the like.

Various treatment planning optimization techniques exist for developingradiation dose patterns, or fluence patterns, for external beamradiation therapy treatment plans. As previously stated, treatmentplanning can include obtaining images of the patient using CT and MRItechnologies. The CT and MRI measurements can be used to determine thelocation of the treatment target of the patient as well as surroundingtissues that are at risk from being irradiated. Organs surrounding thetreatment target, or in the path of the radiation beams, can be called“Organs-at-Risk” (OARs). The area of the patient to which the radiationis intended to be provided can be called the planning treatment target(PTV). OARs and the PTV can have complex three-dimensional shapes thatmake preparing the radiotherapy treatment plan a complex task.

Various computing systems have been developed to facilitate preparationof a treatment plan for patients. Typically, the treatment planningsystems are configured to import three-dimensional images from one ormore diagnostic imaging sources, such as CT scanners, MRI machines, orthe like. The resultant “volume” may then be split into multipledifferent volume elements, or “voxels.” A dosage amount for each volumeelement can then be determined.

During radiotherapy planning, volumetric elements are delineated to betargeted or avoided with respect to the administration of a radiationdose, Once the PTV has been defined, and the OARs have been identified,a responsible radiation oncologist can specify a desired radiation doseto the PTV and the allowable dose to OARs. The planning software canthen produce a treatment plan that attempts to meet the clinicaldosimetric objectives. The treatment plan is the programmed set ofinstructions to the radiation delivery machine, but can be summarizedfor its clinical effect on the patient in terms of dose-volumerelationships that can include a three-dimensional dose matrix. Onecommonly used embodiment of a dose-volume relationship is thedose-volume histogram (DVH) that summarizes the frequency distributionof radiation doses in a particular PTV or OAR structure.

The presently available treatment planning computing systems requirehighly subjective input by numerous medical professionals and rely ontheir level of expertise, biases, and the amount of time the medicalprofessional is able to dedicate to the treatment plan. This can lead tothe unnecessary irradiation of OARs or a missed opportunity to providehigher intensity doses to the PTV.

SUMMARY

In one aspect methods and systems and non-transitory computer programproduct are described. The method can include one or more operations tobe performed by at least one data processor forming at least part of acomputing system. The system can include at least one data processor andat least one memory coupled to the at least one data processor. The atleast one memory can be configured to store instructions, which, whenexecuted, can cause the at least one data processor to perform one ormore operations. The non-transitory computer program product can includeinstructions, that when executed by at least one programmable processor,forming at least part of a computing system, can cause the at least oneprogrammable processor to perform one or more operations.

The one or more operations can include selecting data corresponding tothe spatial position of one or more volume elements of a target patient.Data can be selected that corresponds to a set-up of a radiation therapydelivery system for delivering one or more radiation fields to thepatient. A determination can be triggered to determine an intensity ofradiation fields delivered by the radiation therapy delivery system. Theintensity of the radiation fields can be determined for radiation fieldsthat intersect with individual volume elements of the one or more volumeelements of the patient. The intensity at the volume elements can bedetermined. A three-dimensional radiation dose matrix can be generatedfor the patient. The three-dimensional radiation dose matrix can begenerated based on a predictive model.

In some variations, the one or more operations can include generating aradiation therapy treatment plan for the patient based on thethree-dimensional radiation dose matrix for the patient. Thethree-dimensional radiation dose matrix can include a quantity ofradiation provided to the one or more volume elements of the patient.Data corresponding to one or more radiation therapy clinical objectivescan be received and the generating of a radiation therapy treatment planfor the patient can be based on the data corresponding to the or moreradiation therapy clinical objectives. A radiation therapy treatmentplan can be generated for the patient based on their unique anatomicalstructure.

In some variations, a graphical user interface can be generated forpresentation on a screen of a user device. The user device can beassociated with a medical practitioner displaying the generatedradiation therapy treatment plan for the patient.

In some variations, modifications to the generated radiation therapytreatment plan for the patient can be facilitated. In some variations,modification can be performed via the graphical user interface.

In some variations, the predictive model can be trained using a machinelearning system.

In some variations, the at least one data processor can be included incircuitry that is part of a radiation dose system.

In some variations, the three-dimensional radiation dose matrix for thepatient can be based on the quantity of radiation fields that intersectwith the one or more volume elements of the patient. Basing thethree-dimensional radiation dose matrix for the patient on the quantityof radiation fields that intersect with the one or more volume elementsof the patient can includes selecting, for individual ones of the one ormore volume elements of the patient, a maximum radiation dose. Athree-dimensional radiation dose matrix can be determined for thepatient where the radiation dose experienced for individual ones of theone or more volume elements is less than or equal to the selectedmaximum radiation dose.

In some variations, the data corresponding to the spatial position ofthe one or more volume elements of the patient can include the spatialposition of the one or more volume elements with respect to an organstructure(s) of the patient, a treatment target(s) of the patient, ananatomical structure(s) of the patient, or the like.

In some variations, the spatial position of the one or more volumeelements with respect to the treatment target of the patient can includea distance of the one or more volume elements from an organ structure(s)of the patient, a treatment target(s) of the patient, an anatomicalstructure(s) of the patient, or the like.

In some variations, the spatial position of the one or more volumeelements with respect to the treatment target of the patient can includean orientation of the one or more volume elements with respect to anorgan structure(s) of the patient, a treatment target(s) of the patient,an anatomical structure(s) of the patient, or the like.

In some variations, the data corresponding to the spatial position ofone or more volume elements of a patient can include a matrix of aplurality of volume elements in the vicinity of the treatment target ofthe patient. The data corresponding to the spatial position of one ormore volume elements of a patient can include angular parameters of oneor more volume elements of the patient.

In some variations, the data corresponding to a set-up of a radiationtherapy delivery system for delivering one or more radiation fields tothe patient can include a field angle(s), a field strength(s), a fieldaperture(s), or the like, of the field(s) delivered to the patient.

The data corresponding to a set-up of a radiation therapy deliverysystem for delivering one or more radiation fields to the patient caninclude a position of a couch with respect to one or more elements ofthe radiation therapy delivery system.

In some variations, the machine learning system is a neural network.

In some variations, the three-dimensional radiation dose matrix caninclude a radiation dose to the one or more volume elements of thepatient.

In some variations, the radiation fields can include photon beams, ionbeams, other radiation beams, or the like.

In some variations, the determination of the quantity of radiationfields delivered by the radiation therapy delivery system that willintersect with individual volume elements of the one or more volumeelements of the patient can be limited to the one or more volumeelements outside of the treatment target of the patient, to one or morevolume elements within the treatment target of the patient, or the like.

In some variations, the predictive model can be based on a plurality ofreports that include observed radiation field patterns in one or morevolume elements of prior patients.

In some variations, the one or more volume elements are one or morevoxels.

In another aspect, a means for generating a radiation dose matrix for apatient is described. The means for generating a radiation dose matrixfor a patient can use a predictive model. The radiation dose matrix forthe patient can be based on a spatial position of one or more volumeelements of a target patient. The radiation dose matrix for the patientcan be based on a set-up of a radiation therapy delivery system fordelivering one or more radiation fields to the patient. The means forgenerating a radiation dose matrix for a patient can be configured toperform one or more of the operations described herein.

Implementations of the current subject matter can include, but are notlimited to, systems and methods consistent including one or morefeatures are described as well as articles that comprise a tangiblyembodied machine-readable medium operable to cause one or more machines(e.g., computers, etc.) to result in operations described herein.Similarly, computer systems are also described that may include one ormore processors and one or more memories coupled to the one or moreprocessors. A memory, which can include a computer-readable storagemedium, may include, encode, store, or the like one or more programsthat cause one or more processors to perform one or more of theoperations described herein. Computer implemented methods consistentwith one or more implementations of the current subject matter can beimplemented by one or more data processors residing in a singlecomputing system or multiple computing systems. Such multiple computingsystems can be connected and can exchange data and/or commands or otherinstructions or the like via one or more connections, including but notlimited to a connection over a network (e.g. the Internet, a wirelesswide area network, a local area network, a wide area network, a wirednetwork, or the like), via a direct connection between one or more ofthe multiple computing systems, etc.

The details of one or more variations of the subject matter describedherein are set forth in the accompanying drawings and the descriptionbelow. Other features and advantages of the subject matter describedherein will be apparent from the description and drawings, and from theclaims. While certain features of the currently disclosed subject matterare described for illustrative purposes in relation to an enterpriseresource software system or other business software solution orarchitecture, it should be readily understood that such features are notintended to be limiting. The claims that follow this disclosure areintended to define the scope of the protected subject matter.

DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

The accompanying drawings, which are incorporated in and constitute apart of this specification, show certain aspects of the subject matterdisclosed herein and, together with the description, help explain someof the principles associated with the disclosed implementations. In thedrawings,

FIG. 1 is a diagram of a system having one or more elements consistentwith the current subject matter;

FIG. 2 is an illustration of an example process by which an expectedthree-dimensional radiotherapy dose distribution can be generated fornew patients, the process having one or more elements consistent withthe current subject matter;

FIG. 3A is an illustration of an example of a voxel that share a rangeof boundary distances, forming a shell around the PTV in three planes ofthe patient, the illustration obtained by one or more elementsconsistent with the current subject matter;

FIG. 3B is an illustration of another example of a voxel that share arange of boundary distances, forming a shell around the PTV in threeplanes of the patient, the illustration obtained by one or more elementsconsistent with the current subject matter;

FIG. 3C is an illustration of another example of a voxel that share arange of boundary distances, forming a shell around the PTV in threeplanes of the patient, the illustration obtained by one or more elementsconsistent with the current subject matter;

FIG. 4 is a graph summarizing the one-dimensional measured dose in thevoxels in the range of boundary distances r₁<r_(PTV)<r₂ forming a shellaround PTV, for a given treatment plan, the graph generated by one ormore elements consistent with the current subject matter;

FIG. 5 is an illustration of a three-dimensional parameterization of thespatial location of individual voxels within a particular boundarydistance shell according to a patient-centric coordinate system;

FIG. 6 is an illustration of a one-dimensional differential DVHdetermined based on the DVH prediction of FIG. 4;

FIG. 7 is an illustration of field lines passing through a patient basedon the position of a gantry of a radiation therapy delivery system;

FIG. 8 is an illustration of a schematic of an artificial neural network(ANN) having one or more features consistent with the current subjectmatter;

FIG. 9A is a diagram illustrating a process by which the ANN might betrained, in accordance with one or more features of the presentlydescribed subject matter;

FIG. 9B is an illustration of schematic for generating a radiation doseprediction, according to one or more elements of the presently describedsubject matter;

FIG. 10A shows the minimum distance from PTV boundary to each voxeloutside the PTV for a fixed z position;

FIG. 10B shows the number of photon beams intersecting voxels locatedbetween 3-4.5 mm from the PTV boundary;

FIG. 10C shows PTV mass distribution in lab frame;

FIG. 11 is an illustration of a representation of r_(PTV), θ, x, y, z,and r_(OAR) for an individual voxel;

FIG. 12A shows an illustration of a single shell (6 mm r_(PTV)<9 mm)around the PTV illustrated in FIG. 11.

FIG. 12B shows an illustration of the three-dimensional predictioncapturing the spatial features of the shell;

FIG. 12C shows an illustration of a dose difference map across theregion;

FIG. 12D shows an illustration of a histogram quantifying the accuracyof the prediction on a voxel-by-voxel basis;

FIG. 12E shows a graph of the standard deviation (σ) of the dosedifferences as a function of r_(PTV);

FIG. 13A shows a three-dimensional dose prediction determined manuallyfor a patient with a brain tumor;

FIG. 13B shows a three-dimensional dose prediction for the area shown inFIG. 13A, determined using a system having one or more elementsconsistent with the current subject matter;

FIG. 13C shows a clinically-approved radiotherapy treatment plan;

FIG. 13D shows a three-dimensional dose prediction determined using asystem having one or more elements consistent with the current subjectmatter;

FIG. 14A is a graph showing reduced variation in three-dimensional doseprediction when using methods and systems having one or more elementsconsistent with the presently described subject matter;

FIG. 14B is a graph showing standard deviations of observed data versusthat of the dose difference (doses normalized to prescription dose);and,

FIG. 15A depict examples of GUI consistent with the current subjectmatter;

FIG. 15B depicts another example of a GUI consistent with the currentsubject matter;

FIG. 15C depicts another example of a GUI consistent with the currentsubject matter;

FIG. 15D depicts another example of a GUI consistent with the currentsubject matter;

FIG. 15E depicts another example of a GUI consistent with the currentsubject matter;

FIG. 15F depicts another example of a GUI consistent with the currentsubject matter.

DETAILED DESCRIPTION

The presently available treatment planning computing systems requirehighly subjective input by numerous medical professionals and rely ontheir level of expertise, biases, and the amount of time the medicalprofessional is able to dedicate to the treatment plan. This can lead tothe unnecessary irradiation of OARs or a missed opportunity to providehigher intensity doses to the PTV. Ultimately, plan quality deficienciescan put a significant proportion of patients who should have a low riskof radiation-induced complications at much higher risk for poor outcome.

While knowledge-based planning (KBP) computing systems can offer a meansto eliminate poor quality treatment planning, existing KBP methods stillrely on dose-volume histogram (DVH) prediction at their core. Relianceon DVH-based plan optimizers precludes the treatment design process frombeing driven by a patient's radiation oncologist.

The presently described subject matter provides computing systemscapable of providing highly accurate predictions using three-dimensionaldistributions. The computing systems can be configured to synthesizeanatomy-to-dose correlations from previously treated patients. Thesynthesis can be performed on a volume-element-by-volume-element basis.This allows the radiation oncologist to see a highly accuraterepresentation of an achievable final dose distribution immediatelyafter completion of normal tissue and tumor contouring.

In some variations, the synthesis can be performed on a voxel-by-voxelbasis. A voxel being a volume-element which may be characterized as arectangular object of width, height, and depth corresponding to theresolution of the cardinal axes in a three-dimensional medical image. Avoxel may also be characterized by its size in each dimension and itslocation in space. Consequently, the term voxel may refer to adynamically changing volume depending on how the operator controls thecomputing system.

The presently described subject matter contemplates using theknowledge-based three-dimensional dose prediction to facilitatephysician-driven isodose adjustment by a radiation oncologist, or thelike, according to a patient's unique clinical circumstances.

The subject matter disclosed herein can, in some example embodiments,synthesize information from previously treated radiotherapy patientsinto a system that can make predictions for what radiotherapy dosedistributions will look like in three-dimensions for new treatment plansfor patients based on their unique anatomical features. This can allowclinicians to know what radiotherapy will do for a particular patient,without incurring the time and effort of the planning process, as wellas facilitating both automated treatment planning, optimization methods,and treatment plan quality control.

In some example embodiments, a correlation can be drawn betweengeometric parameters of the patient anatomy and the value of observedradiation dose distributions for clinically treated plans. Geometricparameters of the patient's anatomy can include, for example, theposition and orientation of PTV volume-elements, OAR volume elements,with respect to one or more locations within the patient or theradiation treatment system.

Previous systems have focused almost exclusively on distancecorrelations that yielded dose volume histogram predictions.Fundamentally, these system do not have the ability to describe exactlywhere the radiation dose was being deposited in orientation. The subjectmatter described herein can incorporate angular parameters and furthertreatment geometric information to allow not just a distance-dosecorrelation but a dose-angular correlation as well. Such treatmentgeometric information can include beam strengths, beam widths, beamshapes, beam orientations, or the like. With the ability to predict howthe dose changes with both distance and angular orientation from thetumorous target boundary, the system described herein can create anexpected three-dimensional dose distribution for new patients.

FIG. 1 is a diagram of a system 100 having one or more elementsconsistent with the current subject matter. The system 100 can comprisea computing system such as a platform server 102. The platform server102 can include a personal computing system, a server located at atreatment facility, a server located at a remote location to thetreatment facility, or the like. The platform server 102 can beconfigured to perform one or more operations, such as the operationsdescribed herein. The operations can be performed by one or morecomponents, for example, the radiation fields component 104, radiationmatrix component 106, a therapy plan component 108, or the like. Theplatform server 102 can include one or more processors configured tocause the platform server 102 to perform one or more operations definedby components 104, 106, 108, or the like.

Although FIG. 1 illustrates components 104, 106 and 108 as separatecomponents, the functionality provided or facilitated by components 104,106 and 108 can be performed by a single component, a combination ofcomponents, one or more other components, or the like. System 100 is anexemplary illustration only and is not intended to provide anylimitations.

In some variations, the platform server 102 can be configured tocommunicate with one or more user devices 110. The platform server 102may be configured to communicate with the one or more devices directlyor through a communication network 112. The communication network 112can be a local area network, wide area network, or other network type.

The system 100 can include one or more imaging devices 114. The one ormore imaging devices 114 can include a CT machine, MRI machine, X-raymachine, or the like. Measurement data obtained by the one or moreimaging devices 114 can be obtained by the platform server 102. Themeasurement data can be assimilated by the platform server 102. In somevariations, the measurement data can be assimilated by the one or moreimaging devices 114.

The system 100 can include one or more radiation therapy systems 116.The radiation therapy systems 116 can be configured to implement aradiation therapy plan generated by the platform server 102. Theradiation therapy plan can be transmitted to the one or more radiationtherapy systems 116.

The system 100 can include one or more electronic storage devices 118.The electronic storage device(s) 118 can be associated with the platformserver 102. The electronic storage device(s) 118 can be associated witha data center remote from the platform server 102. The electronicstorage device(s) 118 can store historical information related toradiation therapy patients.

FIG. 2 is an illustration of an example process 200 by which an expectedthree-dimensional radiotherapy dose distribution can be generated fornew patients, the process having one or more elements consistent withthe current subject matter. One or more of the operations described inrelation to process 200 may be performed by one or more components ofsystem 100.

At 202, data can be received that corresponds to the spatial position ofone or more volume elements of a patient. In some variations, the datacan be received by at the platform server 102.

In some variations, the data corresponding to the spatial position ofthe one or more volume elements of the patient can include the spatialposition of the one or more volume elements with respect to organstructures of the patient, OARs, the PTV(s), or the like. In somevariations, the spatial position of the one or more volume elements withrespect to the treatment target of the patient includes a distance ofthe one or more volume elements from an edge of the treatment target ofthe patient, an orientation of the one or more volume elements withrespect to a surface of the treatment target of the patient, or thelike.

The spatial position of one or more volume elements of a patient can beprovided as a matrix of a plurality of volume elements in the vicinityof the treatment target of the patient. The volume elements can beobtained from an analysis of CT, MRI, X-ray, measurements of thepatient, or the like. These can be referred to as three-dimensionalimagery data. The three-dimensional imagery data can be used todistinguish between OARs and PTVs and other boundaries within thepatient. In some variations, the three-dimensional imagery data canstore the information as a set of volume elements. These volume elementsmay be stored as voxels within the three-dimensional imagery data.

FIG. 3 is an illustration of a set of voxels that share a range ofboundary distances, forming a shell around the PTV in three planes ofthe patient, 302, 304 and 306. The range of boundary distances can beprovided by r₁<r_(PTV)<r₂ forming a shell around a PTV. This shell canbe a structure with a differential DVH.

FIG. 4 is a graph 400 summarizing the one-dimensional measured dose inthe voxels in the range of boundary distances r₁<r_(PTV)<r₂ forming ashell around PTV, for a given treatment plan.

FIG. 5 is an illustration 500 of a three-dimensional parameterization ofthe spatial location of individual voxels within a particular boundarydistance shell according to a patient-centric coordinate system. Apatient-centric coordinate system can include superior/inferior,anterior/posterior, and left/right components. The parameterization ofthe spatial location of individual voxels can be defined by azimuthal(θ) and elevation (ϕ) angles. The differential DVH of the boundarydistance shell can thus be mapped to a 2D distribution, as shown in FIG.6. FIG. 6 is an illustration 600 of a one-dimensional differential DVHdetermined based on the DVH prediction of FIG. 4. Three-dimensional doseprediction can include a determination of the two-dimensional dosepattern of FIG. 6, for all shells of the PTV, r_(PTV).

The data corresponding to the spatial position of one or more volumeelements of the patient can include the boundary distance r_(OAR) ofeach volume element to each identified OAR.

In addition to the parameters listed on the preceding page, thesecontributed to the intrinsic voxel geometric parameters by computing theboundary distance r_(OAR) to each OAR.

In some variations, the data corresponding to the spatial position ofthe one or more volume elements of a patient can be obtained from acommercial treatment planning system, or TPS. The information obtainedfrom the TPS can include general information about the treatment such aspatient information, the treatment setup, and the orientation of thetreatment fields with respect to the patient. The information caninclude the outlines of the relevant patient anatomy, including thetreatment target (PTV) and neighboring organs-at-risk (OARs).

In some variations, the spatial position of each dose voxel can becalculated with respect to organ structures. The minimum distance fromthe PTV boundary can be calculated for each voxel that lies outside thePTV. A matrix of voxel points with their spatial coordinates, distancefrom the PTV boundary, and observed dose can be stored in electronicstorage memory for further operations.

At 204, data corresponding to a set-up of a radiation therapy deliverysystem for delivering one or more radiation fields to the patient can bereceived at the at least one data processor. In some variations, thedata corresponding to a set-up of a radiation therapy delivery systemcan be received at the platform server 102.

The data corresponding to a set-up of a radiation therapy deliverysystem for delivering one or more radiation fields to the patient caninclude field angles, field strengths, field widths, or the like, of thefields delivered to the patient. The data corresponding to a set-up of aradiation therapy delivery system for delivering one or more radiationfields to the patient can include a position of the couch, gantry, orthe like, with respect to one or more elements of the radiation therapydelivery system. The data corresponding to a set-up of a radiationtherapy delivery system for delivering one or more radiation fields tothe patient can include a position of the couch, gantry, or the like,with respect to one or more elements of the radiation therapy deliverysystem. The data corresponding to a set-up of a radiation therapydelivery system for delivering one or more radiation fields to thepatient can include an orientation of the couch, gantry, or the like,with respect to one or more elements of the radiation therapy deliverysystem.

In some variations, the data corresponding to a set-up of a radiationtherapy delivery system can include a dose matrix calculated by the TPS,representing the 3D spatial information of how much ionizing radiationis deposited at each point inside the patient.

In some variations, the system can make an accurate prediction for thedose file, including magnitude of radiation absorption for every pointinside of a new patient.

Based on the data corresponding to the treatment setup (e.g., couchangle, gantry angles, field sizes, and the like) the number of fields(e.g., photon beams, or the like) that are seen by each voxel point inthe patient can be calculated. In some variations, this calculation canbe limited to the volume elements residing outside the PTV. In thosevariations, for the volume elements, or voxels, residing within the PTV,the distance from the PTV boundary can be calculated.

In some variations, the calculation to determine the number of fieldsseen by each voxel can be limited to the voxels residing within the PTV.Alternatively or additionally, the treatment of voxels residing withinthe PTV can be different to the treatment of the voxels residing outsidethe PTV. The determination of the amount and intensity of the radiationfields intersecting voxels within the PTV may be performed separatelyfrom the determination of the amount and intensity of the radiationfields intersecting voxels outside the PTV. Separate calculation of thetwo groups of voxels can introduce efficiencies into the generation ofthe three-dimensional radiation dose matrix for the patient.

At 206, the number of radiation fields delivered by the radiationtherapy delivery system that will intersect with individual volumeelements of the one or more volume elements of the patient, can bedetermined. In some variations, determining the number of radiationfields delivered by the radiation therapy delivery system that willintersect with individual volume elements of the one or more volumeelements of the patient can be performed by radiation fields component204. Determining, by the at least one data processor, the number ofradiation fields delivered by the radiation therapy delivery system thatwill intersect with individual volume elements of the one or more volumeelements of the patient can be limited to the one or more volumeelements outside of the treatment target of the patient.

At 208, a three-dimensional radiation dose matrix for the patient can begenerated. The three-dimensional radiation dose matrix can be determinedbased on a predictive model. The determining of the three-dimensionalradiation dose matrix can be based on the number of radiation fieldsthat will intersect with the one or more volume elements of the patient.In some variations, determining of the three-dimensional radiation dosematrix can be performed by radiation matrix component 106. Determining,by the at least one data processor, of the quality of radiation fieldsdelivered by the radiation therapy delivery system that will intersectwith individual volume elements of the one or more volume elements ofthe patient can be limited to the one or more volume elements outside ofthe treatment target of the patient.

In some variations, the three-dimensional radiation dose matrix caninclude the radiation exposure of the one or more volume elements of thepatient. The radiation fields can include photon beams, ion beams, orthe like.

The three-dimensional radiation dose matrix can determine the radiationexposure for volume elements within the patient based on the movement ofan accelerator gantry with respect to the position of the patient, assheen in FIG. 7. FIG. 7 is an illustration 700 of field lines 702passing through a patient 704 based on the position of a gantry 706 of aradiation therapy delivery system.

In some variations the predictive model can be based on a plurality ofreports that include observed radiation field patterns in one or morevolume elements of prior patients. The plurality of reports can bestored, for example, in a voxel database, such as in electronic datastorage 118. Predictive models rely on correlating the observedradiation energy deposition as it relates to the geometry of theindividual patient anatomy, the corresponding geometry of the radiationfield (e.g. photon beam, or the like), and the clinical goals for thepatient's disease. The dose measured at a point in space can becorrelated to the distance from the surface(s) of the PTV(s), the numberof fields propagating through the point, and the orientation of thepoint with respect to the PTV(s).

A voxel database can be generated with all the input variables (e.g.,volume of the PTV, distance from the PTV boundary, number of fieldsintersecting the voxel, directional orientation of voxel with respect tothe PTV surface, principal component axes of the PTV volume,intersecting OAR, and the like). The database can include the targetvariable (e.g., observed dose, or the like) for a plurality of radiationtherapy plans on a plurality of patients.

In some variations the predictive model can be trained using a neuralnetwork, or other processor-based predictive techniques and circuits,neural network circuits, or the like. The predictive model can beconfigured to use historical patient data. The historical patient datacan be stored on electronic storage devices, such as electronic storagedevice 118. In some variations, a neural network can be employed toperform one or more functions on the voxel database. An artificialneural network (ANN) function can be called to train the predictivemodel. FIG. 8 is an illustration of a schematic 800 of an ANN having oneor more features consistent with the current subject matter. The ANN cantrain the predictive model using samples stored and maintained in in thevoxel database.

The trained ANN can be designed to receive a new case as input. The newcase can be represented by a structure set and beam orientationinformation. The trained ANN can be configured to output athree-dimensional dose matrix that represents the expectation for theabsorbed dose for every voxel inside the patient. The clinical intentfor a new case can be matched with the clinical intent of the casesstored in the voxel database.

FIG. 9A is a diagram 900 illustrating a process by which the ANN mightbe trained, in accordance with one or more features of the presentlydescribed subject matter. Patient data sets 902 are introduced into anANN 904. The patient data sets 902 can include a structure set,three-dimensional imagery data, radiation field data, radiation dosedata, or the like. The structure set can include information associatedwith individual volume elements, or voxels, as measured bythree-dimensional imagery systems. Radiation field data can include adirection, size, strength, or the like of the radiation field to beadministered to the patient. The radiation field data can include one ormore attributes of the radiation therapy treatment equipment, such aswhether the equipment include a collimator, or the ability to vary partsof the field during treatment, or the like. Radiation dose data caninclude a desired dose to be delivered to each of the identified volumeelements within the patient. The patient data sets 902 can also includedata associated with observed radiation at the volume elements of thepatient.

In some variations, training of the ANN 904 can be achieved by inputtingdata 902 corresponding to parameters of voxels within a defined distancefrom the boundary of the PTV. Inputs provided to the ANN can includeintrinsic, (i.e. voxel-dependent) and extrinsic (i.e., case-dependent)parameters. Such parameters for individual voxels can include: PTVboundary distance r_(PTV) (1 input), as shown in FIG. 10A which showsthe minimum distance from PTV boundary to each voxel outside the PTV fora fixed z position; the number of intersecting fields (1 input), asshown in FIG. 10B which shows the number of photon beams intersectingvoxels located between 3-4.5 mm from the PTV boundary; azimuthal &elevation angles θ, ϕ, (2 inputs); angles from PTV boundary (2 inputs);Cartesian coordinates x, y, z (3 inputs); OAR boundary distances r_(OAR)(the number of inputs being case-dependent); PTV volume (1 input,extrinsic parameter); or the like.

Ann's can correlate the geometric and plan parameters to the dose from aclinically approved plan for each voxel. Any parameters that couldaffect the dose distribution can be included in the training to improvethe predictive ability. The geometrics of OARs and whole organ systemsand anatomical regions. The distance to PTV (r_(PTV)) captures thegeneral slope of dose gradient outside PTV with lower average dose asr_(PTV) increases. PTV volume (VPTV) is known to influence dosimetric.The parameters can include number of beams (N_(field)) seen by eachvoxel. N_(field) can be obtained from arc angles and can assume thateach beam is conformal to the PTV. The azimuthal (Π) and elevation (θ)angles can be measured from the PTV centroid. Assuming a uniformlydistributed mass, the principal component axis of the PTV can becalculated. The azimuthal (α) and elevation (β) angles in the principalcoordinate system can also be used as inputs to train the ANN. FIG. 10Cshows PTV mass distribution in lab frame. Other parameters, such aselectron density taken from CT images, could be incorporated into thedose prediction.

FIG. 9B is an illustration of schematic for generating a radiation doseprediction, according to one or more elements of the presently describedsubject matter. New patient data sets 908 can be received by the system.The new patient data sets 908 can include information similar to theinformation included in the prior patient data sets 902. The 3D dosemodel 906, generated by the ANN 904 can operate on the new patient datasets 902 to facilitate the generation of a predicted three-dimensionaldose matrix. Manual physician planning 910 can be used to generate athree-dimensional dose matrix. The manual physician planning 910 canaugment the three-dimensional model 906 to provide a physician modifiedthree-dimensional dose matrix.

In some variations, the analysis of the volume elements can be limitedto those volume elements that receive above a threshold amount ofradiation dose (DRx). Limiting the analysis to those volume elementsthat receive above a threshold amount of DRx can reduce thecomputational time to analyze the volume elements. The threshold DRx canbe selected at a level where exposure to the threshold DRx will providelimited harm to the volume elements. An example of the threshold DRx canbe 10% of the DRx.

FIG. 11 is an illustration of a representation 1100 of r_(PTV), θ, x, y,z, and r_(OAR) for an individual voxel. FIG. 11 illustrates a specificexample of a PTV located in the prostate of a patient.

FIGS. 12A-12E are illustrations quantifying the accuracy ofthree-dimensional radiotherapy dose predictions, predicted by a systemhaving one or more elements consistent with the presently describedsubject matter. FIG. 12A shows an illustration 1202 of a single shell (6mm<r_(PTV)<9 mm) around the PTV illustrated in FIG. 11. FIG. 12B showsan illustration 1204 of the three-dimensional prediction capturing thespatial features of the shell. FIG. 12C shows an illustration 1206 of adose difference map across the region. FIG. 12D shows an illustration1208 of a histogram quantifying the accuracy of the prediction on avoxel-by-voxel basis. FIG. 12E shows a graph 1210 of the standarddeviation (σ) of the dose differences as a function of r_(PTV). Asshown, the standard deviation, σ, decreases with incorporation of newgeometric inputs, quantifying the predictive power of each geometricinput provided by a system having one or more elements consistent withthe presently described subject matter.

In some variations, the predictive model can be trained independentlyfor different anatomical regions. For example, the predictive model canbe trained by the ANN for prostate cancer, independently of intracranialcancers, independently of lung cancers, independently of spinal cancers,or the like. In other variations, the predictive model can useanatomical features of PTVs and OARs in one region of the body todetermine a three-dimensional dose matrix for another part of the bodybased on similarities of various anatomical regions within a body.

A three-dimensional dose predictive system can receive a datacorresponding to the spatial position of one or more volume elements ofa patient and data corresponding to a set-up of a radiation deliverysystem, with properly identified PTVs and OARs, and can output athree-dimensional radiation dose matrix for the patient. The lowestcurve of the graph shown in FIG. 9D shows the total predictionuncertainty for a single case as a function of r_(PTV), resulting in5-7% prediction accuracy floor from the range of 0-30 mm.

FIG. 13A shows a three-dimensional dose prediction 1302 determinedmanually for a patient with a brain tumor. FIG. 13B shows athree-dimensional dose prediction 1304 for the area shown in FIG. 13A,determined using a system having one or more elements consistent withthe current subject matter. FIG. 13C shows a clinically approvedradiotherapy treatment plan 1306. FIG. 13D shows a three-dimensionaldose prediction 1308 determined using a system having one or moreelements consistent with the current subject matter. Thethree-dimensional dose prediction shown in FIG. 13D shows the identifieddifferential gradient regions at clinically relevant dose levels(e.g., >50% of 30Gy prescription). The primary OAR in FIGS. 13A-13Dbeing the brainstem.

At 210 a radiation therapy treatment plan can be generated for thepatient based on the three-dimensional radiation dose matrix for thepatient. The radiation therapy treatment plan can be generated by theplatform server 102. In some variations, the radiation therapy treatmentplan can be generated by a user device, such as user device(s) 110. Insome variations, the radiation therapy treatment plan can be generatedby one or more computing systems associated with the radiation therapytreatment system 116. The treatment plan may be based on informationprovided or calculations performed by the platform server 102.

The radiotherapy treatment plan generated at 210, may be generated byperforming one or more optimizations on an existing treatment plan,based on the three-dimensional radiation dose matrix generated at 208.In some variations, the radiation treatment plan generated by thesubject matter described herein can include Volumetric Modulated ArcTherapy (VMAT) sequences. The VMAT sequences can be based on thedetermined voxel-wise knowledge-based predictions.

The presently described subject matter contemplates multiple ways tooptimize the treatment plan based on the generated voxel-wiseknowledge-based predictions. One such method includes introducing beamapertures into the VMAT sequence. An improved VMAT sequence plan can begenerated for each stage of the treatment plan.

A generalized cost function for VMAT or IMRT optimization based on 3Ddose prediction can be provided by:f(D)=Σ_(i)λ({right arrow over (x _(t))},D _(pred)({right arrow over (x_(t))}),δD _(pred)({right arrow over (x _(t))})[D _(i) −D _(pred)({rightarrow over (x _(t))})]²where x_(i) is the position of the i^(th) voxel, D_(pred)(x_(i)) is thepredicted dose at x_(i), δD_(pred) is the prediction uncertainty atx_(i), and λ(x_(i), D_(pred)(x_(i)), δD_(pred)(x_(i))) is a weightingterm. This weighting term can be used in at least two ways: (i) toaccount for the uncertainty in the three-dimensional dose prediction;and/or (ii) to prioritize dose agreement in particular anatomicstructures and/or particular dose levels over others.

In some variations, it may desirable to require a higher level ofagreement with the predicted dose when the three-dimensional doseprediction model has a lower level of uncertainty, and allow for a lowerlevel of agreement if the model has a higher level of uncertainty. Insome variations, it may be desirable to have the most important voxels,e.g., the high-dose voxels of a serial OAR, most closely matching thethree-dimensional dose prediction values. The presently describedsubject matter permits the voxel-wise dose predictions be achievable inthe treatment plan (in contrast with the idealized thresholds typicallyused in treatment plan optimization, such as prescription dose fortargets and zero dose for OARs), making sensitivity to the weights, andthe need for tuning, much less likely to be an issue.

FIG. 14A is a graph 1400 showing reduced variation in three-dimensionaldose prediction when using methods and systems having one or moreelements consistent with the presently described subject matter. Line1402 represents the best possible three-dimensional dose predictionavailable using prior art DVH prediction methods. Data points belowunderneath this line represent three-dimensional dose predictions thatgive more accurate results at all distance intervals from the PTV. FIG.14B is a graph 1404 showing standard deviations of observed data versusthat of the dose difference (doses normalized to prescription dose).Line 1406 represents the best possible prediction with previous methods.Data points under this line represent improved three-dimensional doseprediction. FIG. 14B illustrates that the average residual error isnearly half that as achievable with previous methods.

A graphical user interface (GUI) can be generated for presentation on ascreen of a user device associated with a medical practitioner. The GUIcan be configured to display the generated radiation therapy treatmentplan for the patient. Modifications to the generated radiation therapytreatment plan for the patient can be made via the GUI. Modificationsmay be analyzed the impact of the modifications can be determined forindividual voxels. Notifications can be generated via the GUI to alert amedical practitioner of voxels, e.g. voxels of the OARs of the patient,put at risk by the modifications to the treatment plan.

A system, such as system 100, can be configured to facilitate a medicalpractitioner to reassign the status of particular OARs. In somevariations this can be done on a voxel-by-voxel basis. In othervariations, the status of OARs can be reassigned in-bulk. Theassociation of each voxel with respect to OAR(s) can be known from atleast the operations performed at 206. The medical practitioner, via theGUI, or otherwise, can reassign the status of the voxels associated witha particular OAR. For example, the status of an OAR can be converted toeither the highest priority OAR (maximal dose avoidance) or asunspecified tissue (lowest dose avoidance priority).

The system can be further configured to facilitate “local” modificationof the target coverage, increase the priority of hotspots by adjustingthe voxel weighting term, or the like. As used herein, “local”modification can be performed by a medical practitioner involved in thetreatment of the patient, as opposed to modification by the provider ofsuch systems. In one exemplary variations, such local modifications canbe configured to facilitate dragging of an isodose line presentedthrough a GUI which can convert the movement of the isodose line to anassociated adjustment of the underlying geometric parametersX_(geo)→X_(geo)=X_(geo)+δX_(geo). This can be performed by generating anappropriate translation from the dragging of the isodose line on the GUIto the desired dose action in the treatment plan. Each isodoserelocation can be converted to a reversible reassignment of surroundingvoxel geometric properties. Voxels within a spherical neighborhood of adefined radius can be altered based on the isodose relocation. A desireddose alteration can define the range of the effect of the indicatedisodose move.

The three-dimensional radiation dose matrix for the patient can bere-generated based on the modification to the isodose location. In theevent that the radiation dose as predicted by the three-dimensionalradiation dose matrix does not match that desired by the medicalpractitioner, the defined radius of the sphere can be iterativelymodified to achieve the desired result. A radiation therapy treatmentplan can, in turn, be regenerated based on the modifiedthree-dimensional radiation dose matrix.

The presently described subject matter can provide medical practitionercontrol over the radiation therapy treatment plan and dose distribution,limited only by the delivery technique (e.g. VMAT) and the physicallimitations of dose gradients with megavoltage photons.

FIG. 15 is a diagram of a GUI 1500 showing representations of thesequence of changes to the GUI based on input from a medicalpractitioner. GUI diagrams (c), (d) and (e) of FIG. 15 representcalculations performed by a computing system, such as platform server102, user devices 110, devices associated with the radiation therapytreatment system 116, or the like. GUI diagrams (a), (b) and (f) of FIG.15 represent GUIs that can be presented a physician that is modifyingthe generated radiotherapy treatment plan.

The particular set of GUI diagrams shown in FIG. 15 is for a case wherea patient's clinical circumstances demand that the 50% line be excludedfrom the OAR, even at the expense of PTV coverage. At (a) arepresentation of a treatment plan is provided on a GUI. At (b) aphysician selects the 50% isodose line (dotted arrow) and drags it tothe desired location (solid arrow). At (c) this input via the GUI can betranslated, for example by platform server 102, user device(s) 110, orthe like, to a corresponding change in geometric parameters, until at(d) an appropriate X_(geo)+δX_(geo) is found. At (e) the alteredgeometric parameters can result in a new predicted dose D_(pred)′,generated by one or more elements of the presently described subjectmatter. At (f) the GUI can be updated with a new deliverable dosedistribution D_(deliv)′.

Without in any way limiting the scope, interpretation, or application ofthe claims appearing herein, a technical effect of one or more of theexample embodiments disclosed herein may include the ability to predictthree-dimensional dose distributions for the treatment planning processof a patient in need of radiation therapy, by not only providinginformation as to the effectiveness of a clinician's plans but alsoproviding where inside the patient the dose distribution could beimproved.

Without in any way limiting the scope, interpretation, or application ofthe claims appearing herein, a technical effect of one or more of theexample embodiments disclosed herein may include the ability to providetreatment plans based on a volume-element-by-volume element optimizationof the radiation distribution plan, eliminating unnecessary exposure ofvolume elements to radiation, and providing the ability to increase theradiation dose to the PTV.

Without in any way limiting the scope, interpretation, or application ofthe claims appearing herein, a technical effect of one or more of theexample embodiments disclosed herein may include the ability to provideaccurate three-dimensional dose predictions in the treatment planningprocess, even in response to significantly altered volume elementmodification, or tagging.

Without in any way limiting the scope, interpretation, or application ofthe claims appearing herein, a technical effect of one or more of theexample embodiments disclosed herein may include providing predicteddose distribution that give clinicians a full picture of expectedradiotherapy dose deposition that can be reviewed and evaluated justlike a deliverable plan due to the retention of the spatial information.This can provide information so whether a plan can be improved and alsowhere the plan should be improved.

One or more aspects or features of the subject matter described hereincan be realized in digital electronic circuitry, integrated circuitry,specially designed application specific integrated circuits (ASICs),field programmable gate arrays (FPGAs) computer hardware, firmware,software, and/or combinations thereof. These various aspects or featurescan include implementation in one or more computer programs that areexecutable and/or interpretable on a programmable system including atleast one programmable processor, which can be special or generalpurpose, coupled to receive data and instructions from, and to transmitdata and instructions to, a storage system, at least one input device,and at least one output device. The programmable system or computingsystem may include clients and servers. A client and server aregenerally remote from each other and typically interact through acommunication network. The relationship of client and server arises byvirtue of computer programs running on the respective computers andhaving a client-server relationship to each other.

These computer programs, which can also be referred to as programs,software, software applications, applications, components, or code,include machine instructions for a programmable processor, and can beimplemented in a high-level procedural and/or object-orientedprogramming language, and/or in assembly/machine language. As usedherein, the term “machine-readable medium” refers to any computerprogram product, apparatus and/or device, such as for example magneticdiscs, optical disks, memory, and Programmable Logic Devices (PLDs),used to provide machine instructions and/or data to a programmableprocessor, including a machine-readable medium that receives machineinstructions as a machine-readable signal. The term “machine-readablesignal” refers to any signal used to provide machine instructions and/ordata to a programmable processor. The machine-readable medium can storesuch machine instructions non-transitorily, such as for example as woulda non-transient solid-state memory or a magnetic hard drive or anyequivalent storage medium. The machine-readable medium canalternatively, or additionally, store such machine instructions in atransient manner, such as for example, as would a processor cache orother random access memory associated with one or more physicalprocessor cores.

To provide for interaction with a user, one or more aspects or featuresof the subject matter described herein can be implemented on a computerhaving a display device, such as for example a cathode ray tube (CRT) ora liquid crystal display (LCD) or a light emitting diode (LED) monitorfor displaying information to the user and a keyboard and a pointingdevice, such as for example a mouse or a trackball, by which the usermay provide input to the computer. Other kinds of devices can be used toprovide for interaction with a user as well. For example, feedbackprovided to the user can be any form of sensory feedback, such as forexample visual feedback, auditory feedback, or tactile feedback; andinput from the user may be received in any form, including, but notlimited to, acoustic, speech, or tactile input. Other possible inputdevices include, but are not limited to, touch screens or othertouch-sensitive devices such as single or multi-point resistive orcapacitive track pads, voice recognition hardware and software, opticalscanners, optical pointers, digital image capture devices and associatedinterpretation software, and the like.

The subject matter described herein can be embodied in systems,apparatus, methods, and/or articles depending on the desiredconfiguration. The implementations set forth in the foregoingdescription do not represent all implementations consistent with thesubject matter described herein. Instead, they are merely some examplesconsistent with aspects related to the described subject matter.Although a few variations have been described in detail above, othermodifications or additions are possible. In particular, further featuresand/or variations can be provided in addition to those set forth herein.For example, the implementations described above can be directed tovarious combinations and subcombinations of the disclosed featuresand/or combinations and subcombinations of several further featuresdisclosed above. In addition, the logic flows depicted in theaccompanying figures and/or described herein do not necessarily requirethe particular order shown, or sequential order, to achieve desirableresults. Other implementations may be within the scope of the followingclaims.

What is claimed is:
 1. A method to be performed by at least one dataprocessor forming at least part of a computing system, the methodcomprising: selecting, by the at least one data processor, a first datacorresponding to a spatial position of one or more volume elements of apatient; selecting, by the at least one data processor, a second datacorresponding to a set-up of a radiation therapy delivery system fordelivering one or more radiation fields to the patient; applying, by theat least one data processor, a predictive model to determine a quantityof radiation delivered by the radiation therapy delivery system havingthe set-up indicated by the second data to the one or more volumeelements of the patient having the spatial position indicated by thefirst data, the predictive model determining the quantity of radiationdelivered to the one or more volume elements by at least correlating afirst geometry of the one or more volume elements and a second geometryof the one or more radiation fields to an observed radiation energydeposition; and generating, by the at least one data processor, athree-dimensional radiation dose matrix indicating the quantity ofradiation delivered by the radiation therapy delivery system to the oneor more volume elements of the patient.
 2. The method of claim 1,wherein the quantity of radiation delivered by the radiation therapydelivery system is determined based on a quantity of the one or moreradiation fields that intersect with the one or more volume elements ofthe patient.
 3. The method of claim 2, further comprising: determining,based at least on the three-dimensional radiation dose matrix, whetherthe one or more volume elements of the patient experiences a radiationdose exceeding a maximum radiation dose associated with each of the oneor more volume elements.
 4. The method of claim 1, wherein the firstdata includes the spatial position of the one or more volume elementswith respect to a treatment target of the patient.
 5. The method ofclaim 4, wherein the first data includes a distance of the one or morevolume elements from the treatment target of the patient.
 6. The methodof claim 1, wherein the first data includes the spatial position of theone or more volume elements with respect to an anatomical structure ofthe patient.
 7. The method of claim 1, wherein the first data includes amatrix of a plurality of volume elements in a vicinity of a treatmenttarget of the patient.
 8. The method of claim 1, wherein the second dataincludes one or more field angles of the one or more radiation fields.9. The method claim 1, further comprising: determining, by the at leastone data processor, a quantity of the one or more radiation fields thatwill intersect with the one or more volume elements outside of atreatment target of the patient.
 10. The method claim 1, furthercomprising: determining, by the at least one data processor, a quantityof the one or more radiation fields that will intersect with the one ormore volume elements within a treatment target of the patient.
 11. Asystem comprising: at least one data processor; at least one memorycoupled to the at least one data processor, the at least one memorystoring instructions, which, when executed, cause the at least one dataprocessor to perform operations comprising: receiving a first datacorresponding to a spatial position of one or more volume elements of apatient; receiving a second data corresponding to a set-up of aradiation therapy delivery system for delivering one or more radiationfields to the patient; applying a predictive model to determine aquantity of radiation delivered by the radiation therapy delivery systemhaving the set-up indicated by the second data to the one or more volumeelements of the patient having the spatial position indicated by thefirst data, the predictive model determining the quantity of radiationdelivered to the one or more volume elements by at least correlating afirst geometry of the one or more volume elements and a second geometryof the one or more radiation fields to an observed radiation energydeposition; and generating a three-dimensional radiation dose matrixindicating the quantity of radiation delivered by the radiation therapydelivery system to the one or more volume elements of the patient. 12.The system of claim 11, wherein the quantity of radiation delivered bythe radiation therapy delivery system is determined based on a quantityof the one or more radiation fields that intersect with the one or morevolume elements of the patient.
 13. The system of claim 12, wherein theat least one data processor is further caused to perform operationscomprising: determining, based at least on the three-dimensionalradiation dose matrix, whether the one or more volume elements of thepatient experiences a radiation dose exceeding a maximum radiation doseassociated with each of the one or more volume elements.
 14. The systemof claim 11, wherein the first data includes the spatial position of theone or more volume elements with respect to a treatment target of thepatient.
 15. The system of claim 14, wherein the first data includes adistance of the one or more volume elements from the treatment target ofthe patient.
 16. The system of claim 11, wherein the first data includesthe spatial position of the one or more volume elements with respect toan anatomical structure of the patient.
 17. The system of claim 11,wherein the first data includes a matrix of a plurality of volumeelements in a vicinity of a treatment target of the patient.
 18. Thesystem of claim 11, wherein the second data includes one or more fieldangles of the one or more radiation fields for delivery to the patient.19. The system claim 11, wherein the at least one data processor isfurther caused to perform operations comprising: determining a quantityof the one or more radiation fields that will intersect with the one ormore volume elements outside of a treatment target of the patient. 20.The system claim 11, wherein the at least one data processor is furthercaused to perform operations comprising: determining, by the at leastone data processor, a quantity of the one or more radiation fields thatwill intersect with the one or more volume elements within a treatmenttarget of the patient.
 21. The method of claim 1, wherein the predictivemodel determines the quantity of radiation delivered to the one or morevolume elements by at least correlating a dose of radiation measured ata point in space, a distance from the point in space to a surface of theone or more volume elements, a quantity of radiation fields propagatingthrough the point in space, and an orientation of the point in spacerelative to the one or more volume elements.
 22. The method of claim 1,wherein the first data includes one or more of a field angle, a fieldstrength, a field width, a couch position, or a gantry position.